Backtest Analytics Dashboard Development

Backtest Analytics Dashboard Development

Typical problem: a strategist runs a backtest in Python (backtrader, bt, vectorbt), gets JSON with thousands of rows of OHLCV and signals, and sends it to Excel. A proper backtest dashboard isn't about "pretty charts"—it's a tool for quickly identifying problems: overfitting, survivorship bias, incorrect slippage. Visualization should make problems obvious.

Key Metrics and Their Display

Equity Curve

Main chart—equity curve with drawdown panel underneath. Not just a balance line, but:

• Equity line (portfolio value in USD/BTC)
• Benchmark line (buy & hold of the same asset)
• Underwater plot (current drawdown depth in %)

If the equity curve is "smooth" relative to the benchmark—possible sign of overfitting or look-ahead bias in data.

Strategy Statistics

interface BacktestStats {
  totalReturn: number;        // %
  annualizedReturn: number;   // %
  sharpeRatio: number;        // > 1 acceptable, > 2 good
  sortinoRatio: number;       // downside volatility only
  maxDrawdown: number;        // % from peak
  maxDrawdownDuration: number; // days
  winRate: number;            // % of profitable trades
  profitFactor: number;       // gross profit / gross loss
  avgWin: number;             // average winning trade
  avgLoss: number;            // average losing trade
  totalTrades: number;
  calmarRatio: number;        // annualizedReturn / maxDrawdown
}

Display these metrics in a summary panel with large font, color-coded (red/green relative to thresholds).

Technical Stack

Frontend

React + Recharts or TradingView Lightweight Charts for financial data. TradingView Lightweight Charts—specialized library with candlestick, volume, and overlay indicator support out of the box. Recharts is more flexible for custom non-standard visualizations.

import { createChart, ColorType, LineStyle } from 'lightweight-charts';

function EquityCurve({ equityData, benchmarkData }: Props) {
  const chartContainerRef = useRef<HTMLDivElement>(null);
  
  useEffect(() => {
    const chart = createChart(chartContainerRef.current!, {
      width: chartContainerRef.current!.clientWidth,
      height: 400,
      layout: { background: { type: ColorType.Solid, color: '#1a1a2e' }, textColor: '#d1d5db' },
      grid: { vertLines: { color: '#2d3748' }, horzLines: { color: '#2d3748' } },
      timeScale: { timeVisible: true },
    });
    
    const equitySeries = chart.addLineSeries({ color: '#10b981', lineWidth: 2 });
    const benchmarkSeries = chart.addLineSeries({ color: '#6366f1', lineWidth: 1, lineStyle: LineStyle.Dashed });
    
    equitySeries.setData(equityData);
    benchmarkSeries.setData(benchmarkData);
    
    chart.timeScale().fitContent();
    
    return () => chart.remove();
  }, [equityData, benchmarkData]);
  
  return <div ref={chartContainerRef} />;
}

Trades Table

Trades table with sorting and filtering—TanStack Table (react-table v8). For thousands of rows—mandatory virtualization via @tanstack/react-virtual:

import { useVirtualizer } from '@tanstack/react-virtual';

// Render only visible rows
const rowVirtualizer = useVirtualizer({
  count: trades.length,
  getScrollElement: () => parentRef.current,
  estimateSize: () => 40, // row height
  overscan: 10,
});

Without virtualization, a 10,000-row table will freeze the UI for several seconds on render.

Data Storage and Loading

Backtest results—structured JSON. For large backtests (1m data over multiple years = tens of MB), loading everything at once isn't practical. Schema:

interface BacktestResult {
  id: string;
  metadata: BacktestMetadata;   // strategy configuration
  stats: BacktestStats;          // aggregated statistics
  equityCurve: TimeseriesPoint[]; // daily points (usually < 5000)
  trades: Trade[];                // list of trades
  // Don't store OHLCV data—load separately on demand
}

API endpoint with pagination for trades + separate endpoint for equity curve. First render—show stats + equity curve (cheap), load trades on request.

Comparing Multiple Strategies

Powerful feature—overlay equity curves of multiple backtests on one chart. User selects 2-5 strategies from a list, sees their comparison visually and in a parameters table:

interface ComparisonView {
  strategies: Array<{
    id: string;
    name: string;
    color: string;
    stats: BacktestStats;
    equityCurve: TimeseriesPoint[];
  }>;
}

Correlation matrix between strategies—important for portfolio construction. If two strategies are highly correlated, there's no point holding both.

Monte Carlo Visualization

Monte Carlo simulation (shuffling trade order N times) shows the range of possible outcomes. Displayed as a fan chart—center line of median outcome + bands for 10th/90th percentiles:

// Generate fan chart data
const percentiles = monteCarloRuns.reduce((acc, run, idx) => {
  run.forEach((point, t) => {
    if (!acc[t]) acc[t] = [];
    acc[t].push(point.equity);
  });
  return acc;
}, [] as number[][]).map(values => ({
  p10: percentile(values, 10),
  p50: percentile(values, 50),
  p90: percentile(values, 90),
}));

Development Timeline

Equity curve + stats + trades table with virtualization — 2 days. Add strategy comparison + Monte Carlo fan chart + period filtering — 3-5 days total.